10Finishing Touches: Creating Narratives with Your Datavis
Introduction
Up until now, we have concentrated on the techniques for making charts and maps from data sources. This week, we’ll concentrate on what I’m calling the ‘finishing touches’: the final parts of your charts which will make them more attractive, and most importantly, readable, to viewers.
These can be divided into two categories:
Elements ‘extra’ to the chart itself, such as titles (main titles, subtitles, titles on the axes), the background color, and guide lines.
Elements within the chart such as labels and annotations.
Chart titles
The easiest way to improve the readability of your chart is the add titles to help the reader. This is dependent on context, too. It’s often not required or recommended to add a chart title in an academic book or article, but a chart in a report or piece of data journalism should usually have a chart.
A good principle is to try to make your chart stand on its own without further explanation, as far as possible. Even if your visualisation is embedded within a report, readers often skip to the chart first without looking at the underlying context. And charts often get separated from their context if they are shared on social media, for example.
For a chart to stand alone, consider adding the following:
An eye-catching title, which explains the ‘headline’ of your chart.
A subtitle, with further explanation
A caption, which gives other relevant information, particularly the data source, or any necessary caveats to the data.
Titles (and labels) can be added to charts in ggplot by adding the code + labs(). Within + labs(), you can specify title =, subtitle =, and caption =, which are fairly self-explanatory. Make sure to include the text of your label within quotation marks. If you would like to spread your title across multiple lines, you can specify a line break by adding the code /n within your title.
Let’s demonstrate how to make charts more readable and informative with an example.
Take this basic chart which charts the number of asylum seekers by country of origin to Germany, using the UNCHR data and R package. I’ve made a chart which visualises as a line the count of refugees from each country of origin , for the years 2000 to 2020:
options(scipen =999999) # this means large numbers will not be drawn as scientific notation# load the UNCHR data or package and tidyverse librarylibrary(tidyverse)
Warning: package 'tidyverse' was built under R version 4.2.3
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ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
load('population.rda')# first, create a count by year and country of origin:all = population %>%filter(coa_iso =='DEU') %>%#filter to just Germany as country of arrivalgroup_by(year, coo) %>%summarise(n =sum(asylum_seekers)) %>%# sum the total number of asylum seekers for each origin and yearfilter(year %in%c(2000:2020)) # filter to only include the years 2000-2020
`summarise()` has grouped output by 'year'. You can override using the
`.groups` argument.
# next make the chart. ggplot() +geom_line(data = all, aes(x = year, y = n, group = coo)) # if we specify group as the country of origin, it will draw each as a separate line.
First, let’s add a title, subtitle, and caption:
ggplot() +geom_line(data = all, aes(x = year, y = n, group = coo)) +labs(title ="Asylum seekers to Germany\nby country of origin", subtitle ="2000-2020 inclusive", caption ="Data from https://www.unhcr.org/refugee-statistics/")
We can also use labs() to specify new names for the x and y axes, or removing one by setting to NULL:
ggplot() +geom_line(data = all, aes(x = year, y = n, group = coo)) +labs(title ="Asylum seekers to Germany\nby country of origin",x =NULL,y ="Number seeking asylum")
Exercises
Copy the chart into a new cell and add the following:
Add a subtitle with the text “2000-2020 inclusive”,
Using the default settings will make an accurate representation of your data but there are lots of ways to make a chart more readable.
Each visual part of a chart is known as an element, and we can make changes to each part separately. This is done by adding +theme() plus some extra code as a layer to your chart.
Let’s see how this works with an example, first. I want to change the size of the x and y axes text in the chart we made above
To do this, I first add a new layer with + theme() to my existing plot.
Next, within this theme(), I add:
The plot element I would like to change, which in this case is axis.title followed by an = sign. We can also specify separate using axis.title.x or axis.title.y.
The type of element it is (we’ll come back to this), either element_text(), element_rect(), element_line() or, if I want to remove it entirely, element_blank().
In this case, it is a text element, so we use element_text().
Next, within this element_text(), we specify the change we want to make. For a text element, we can change the size, the font (using family) and whether it is bold or italic (using face).
To change the size of the text to 16, use the following full line of code:
ggplot() +geom_line(data = all, aes(x = year, y = n, group = coo)) +labs(title ="Asylum seekers to Germany\nby country of origin", subtitle ="2000-2020 inclusive", caption ="Data from https://www.unhcr.org/refugee-statistics/",x =NULL,y ="Number seeking asylum") +theme(axis.title =element_text(size =16))
Different types of elements need to be specified in different ways, and have different aspects which can be adjusted. Below is a full list of the plot parts and their related element types:
Exercises:
Copy and paste the chart above into a new cell. Make the following changes:
Change the panel background fill to lightblue.
Change the size of the title to 24, and the ‘face’ to bold.
Change the panel grid to the linetype ‘dashed’.
Drawing readers to the data in your chart
The chart above might be a faithful representation of the data, but it doesn’t really tell a story.
To do this, we’ll draw viewers’ attention to particular parts of the data using a few techniques. These use a cognitive process known as ‘pre-attentive processing’, meaning we are drawn to certain visual elements before others. Highlighting using color and annotations are ways to use this to our advantage.
Highlighting using color
One effective method to draw attention to certain elements is to highlight using colours. This can be done fairly easily using our existing toolset. The set of steps are as follows:
Start with your basic chart. Perhaps change colors or transparency to make the majority of the data ‘fade’ into the background.
Next create new datasets containing only the data you wish to highlight.
Add these as layers to your existing chart
Change the colors of these new elements
For instance, let’s highlight asylum seekers from Syria within the plot. We’ll add the data for Syria as a new geom_line(), and we’ll specify a larger size and a different color.
# first all the data (we made this earlier but just to highlight)all = population %>%filter(coa_iso =='DEU') %>%group_by(year, coo) %>%summarise(n =sum(asylum_seekers)) %>%filter(year %in%c(2000:2020))
`summarise()` has grouped output by 'year'. You can override using the
`.groups` argument.
# now a new dataset but with an extra filtersyria = population %>%filter(coa_iso =='DEU'& coo_iso =='SYR') %>%group_by(year, coo) %>%summarise(n =sum(asylum_seekers)) %>%filter(year %in%c(2000:2020))
`summarise()` has grouped output by 'year'. You can override using the
`.groups` argument.
# create the plotggplot() +geom_line(data = all, aes(x = year, y = n, group = coo), alpha = .5) +#reduce the transparency of the other linesgeom_line(data =syria, aes(x = year, y = n), size =1.5, color ='forestgreen') +# add a new line and specify size/colorlabs(title ="Asylum seekers to Germany\nby country of origin", subtitle ="2000-2020 inclusive", caption ="Data from https://www.unhcr.org/refugee-statistics/",x =NULL,y ="Number seeking asylum") +theme(axis.title =element_text(size =16))
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
Exercises:
Add a second line with a different color, this time for asylum seekers with a country of origin Afghanistan.
Adding text and annotations
Another really useful technique for creating a narrative in your data visualisation is to add annotations. This is usually to draw attention to particular aspects of the data, or to explain or give context. The idea is to guide your reader towards the story in your data.
There are quite a few ways of adding text. The simplest way is to add an annotation with a layer called annotate(). This allows us to add text but also shapes or lines.
To create a text annotation, we need to specify a few things within annotate():
The type of annotation, in this case text.
The x and y position the annotation should be placed. This is done manually and usually requires a bit of experimentation.
The actual label to be displayed
Optionally, we can specify color, size, face, family and so forth…
Let’s add a label to show our readers that the highlighted line is Syria
ggplot() +geom_line(data = all, aes(x = year, y = n, group = coo), alpha = .5) +geom_line(data =syria, aes(x = year, y = n), size =1.5, color ='forestgreen') +annotate('text', x =2014, y =75000, label ='Syria', color ='forestgreen', size =5) +# add the annotationlabs(title ="Asylum seekers to Germany\nby country of origin", subtitle ="2000-2020 inclusive", caption ="Data from https://www.unhcr.org/refugee-statistics/",x =NULL,y ="Number seeking asylum") +theme(axis.title =element_text(size =16))
Exercises
Add a similar label for Afghanistan. Find a suitable place on the chart where it will be readable
Adding lines as annotations
Another useful technique for adding contextual information, particularly with line charts, is to add vertical lines highlighting particular points in time.
This is done using another element, called geom_vline(). In this case, we specify where the line should be placed using xintercept. Optionally, we can set the size, the linetype (e.g. to dashed), and the transparency using alpha.
Let’s add a line to the chart to highlight an important milestone in the Syrian civil war: the beginning of revolts in March 2011. Note that because the data is not in date format but simply numbers, we have to add the line at 2011.25 to make it approximately March 2011.
ggplot() +geom_line(data = all, aes(x = year, y = n, group = coo), alpha = .5) +geom_line(data =syria, aes(x = year, y = n), size =1.5, color ='forestgreen') +annotate(geom ='text', x =2014, y =75000, label ='Syria', color ='forestgreen', size =5) +geom_vline(xintercept =2011.25, linetype ='dashed') +# add the line, specifying the linetype. labs(title ="Asylum seekers to Germany\nby country of origin", subtitle ="2000-2020 inclusive", caption ="Data from https://www.unhcr.org/refugee-statistics/",x =NULL,y ="Number seeking asylum") +theme(axis.title =element_text(size =16))
Exercises:
Add a similar line for September 2015, when the German government announced that asylum seekers would be welcomed in Germany.
Text annotations and lines
On their own, these lines are not enough. We can also add labels to the line. For this, we return to the annotate() element.
First, let’s draw a label in an empty area of the chart, using annotate().
ggplot() +geom_line(data = all, aes(x = year, y = n, group = coo), alpha = .5) +geom_line(data =syria, aes(x = year, y = n), size =1.5, color ='forestgreen') +annotate(geom ='text', x =2014, y =75000, label ='Syria', color ='forestgreen', size =5) +geom_vline(xintercept =2011.25, linetype ='dashed') +annotate(geom ='text', x =2007, y =110000, label ="Beginning of Syrian Revolt") +labs(title ="Asylum seekers to Germany\nby country of origin", subtitle ="2000-2020 inclusive", caption ="Data from https://www.unhcr.org/refugee-statistics/",x =NULL,y ="Number seeking asylum") +theme(axis.title =element_text(size =16))
Next we can draw a curved line to connect the text label to the vertical line.
Again, we use annotate(). This time, the geom type is set to curve. When we make a curve, we need to specify the x beginning and end using x and xend, and the y beginning and end, using y and yend.
ggplot() +geom_line(data = all, aes(x = year, y = n, group = coo), alpha = .5) +geom_line(data =syria, aes(x = year, y = n), size =1.5, color ='forestgreen') +annotate(geom ='text', x =2014, y =75000, label ='Syria', color ='forestgreen', size =5) +geom_vline(xintercept =2011.25, linetype ='dashed') +annotate(geom ='text', x =2007, y =110000, label ="Beginning of Syrian Revolt") +annotate(geom ='curve', x =2007, y =100000, xend =2011, yend =65000,arrow =arrow(length =unit(0.3, 'cm'), type ='closed')) +labs(title ="Asylum seekers to Germany\nby country of origin", subtitle ="2000-2020 inclusive", caption ="Data from https://www.unhcr.org/refugee-statistics/",x =NULL,y ="Number seeking asylum") +theme(axis.title =element_text(size =16))
Optionally, we can add an arrow to the end of the line. The syntax starts to get a bit complicated, so copy and paste is your friend!
First, specify to draw an arrow by placing arrow =within the annotate(). Next, we use the following code to tell it what arrow to draw:
arrow = arrow(length = unit(.3, 'cm'), type = 'closed')
Within arrow(), we specify the length of the arrow, within unit() where we also specify what time of unit (e.g. cm, mm, in). Lastly, we can specify either a ‘closed’ or ‘open’ arrow type.
ggplot() +geom_line(data = all, aes(x = year, y = n, group = coo), alpha = .5) +geom_line(data =syria, aes(x = year, y = n), size =1.5, color ='forestgreen') +annotate(geom ='text', x =2014, y =75000, label ='Syria', color ='forestgreen', size =5) +geom_vline(xintercept =2011.25, linetype ='dashed') +annotate(geom ='text', x =2007, y =110000, label ="Beginning of Syrian Revolt") +annotate(geom ='curve', x =2007, y =100000, xend =2011, yend =65000, # add the arrow =arrow(length =unit(0.3, 'cm'), type ='closed')) +labs(title ="Asylum seekers to Germany\nby country of origin", subtitle ="2000-2020 inclusive", caption ="Data from https://www.unhcr.org/refugee-statistics/",x =NULL,y ="Number seeking asylum") +theme(axis.title =element_text(size =16))
Exercises
For both of these, use copy and paste where possible - start by copying in the identical code, and then make changes to the values as necessary.
Add a text label for the second highlighted milestone. Find a suitable place on the chart (it can also be to the right)
Create a connecting line from the label to the vertical line. Optionally, add an arrow.
If time: interactive charts
Another way to make more readable charts is to include interactive elements. This can help viewers to find interesting points in the data by hovering and clicking, or by zooming in. This negates the need for annotations and labels in many cases, meaning we can make less cluttered visualisations.
These interactive charts will display in the same .html documents we have used for the weekly exercises and assignments.
In R, we can use a library called plotly to easily turn our visualisations interactive.
First, create a regular ggplot plot. Importantly, you should name this plot in your environment by setting a name followed by =.
We’ll remove some unnecessary elements, such as the labels and lines, but leave in the colored extra line and the titles.
p =ggplot() +geom_line(data = all, aes(x = year, y = n, group = coo), alpha = .5) +geom_line(data =syria, aes(x = year, y = n), size =1.5, color ='forestgreen') +labs(title ="Asylum seekers to Germany\nby country of origin", subtitle ="2000-2020 inclusive", caption ="Data from https://www.unhcr.org/refugee-statistics/",x =NULL,y ="Number seeking asylum") +theme(axis.title =element_text(size =16))
Now, load the library plotly. If it’s not installed, install using `install.packages(‘plotly’).
You can make plots directly in plotly using this syntax, or you can use a function called ggplotly() to automatically convert your static chart. Simply pass your chart name to the function:
library(plotly)
Warning: package 'plotly' was built under R version 4.2.3
Attaching package: 'plotly'
The following object is masked from 'package:ggplot2':
last_plot
The following object is masked from 'package:stats':
filter
The following object is masked from 'package:graphics':
layout
ggplotly(p)
Interactive maps with tmap
Another feature to improve your data visualisations is to use interactive maps. Again, there are several ways to do this, including the package Leaflet.
The easiest way to create a map is with a package called tmap. This takes an sf object (similar to those we created in earlier weeks), and allows you to make a nice map using a syntax fairly similar to ggplot. Optionally, you can make these maps interactive.
To demonstrate this, I’ll use a dataset from the Shakespeare & Co project at Princeton, which contains information on the locations of members of the famous Shakespeare & Co bookshop and lending library in Paris.
First, install and load the tmap package.
library(tmap)
Warning: package 'tmap' was built under R version 4.2.3
Breaking News: tmap 3.x is retiring. Please test v4, e.g. with
remotes::install_github('r-tmap/tmap')
